Doceree is the world's only AI-powered Operating System for healthcare marketing. We aim to be a catalyst for change, improving healthcare by enabling more meaningful interactions with HCPs. Our patented AI brings deeper context to every HCP touchpoint-understanding who HCPs are, what they're exploring, how they engage, and when they make decisions, all in a privacy-compliant way. With this enriched context, every message becomes clearer, more relevant, and more likely to drive better healthcare outcomes. In our journey of 5 years, we have earned four patents for innovation and even won a Silver award at the Cannes Lions, one of the world's top advertising honors. Over the years, we've also had the opportunity to work with 115+ pharma companies and collaborate with 30+ media publications, 150+ EHR platforms, 2,000+ publisher networks, and 35+ hospitals and health systems. Our ecosystem connects us with more than 6 million verified doctors worldwide. Today, Doceree operates across 25+ countries, with teams based in New Jersey, London, and India.
Technology can connect the fragmented healthcare ecosystem to deliver information when it is most needed to improve patients' outcomes. We are expanding our footprints across the globe and enhancing our services, offering, and developing new products and solutions to address the unmet needs of industry.
Doceree is building the first proactive intelligence layer for pharma brand teams - an agentic AI system that turns clinical intent signals, campaign data, and market context into a daily brief that drives a decision in under five minutes. We are hiring AI Engineers to build the systems behind that surface.
You will design and ship production AI systems - LLM-powered pipelines, agentic workflows, retrieval over heterogeneous healthcare data, evaluation harnesses, and the orchestration layer that sits between our signal foundation and the brand manager's morning brief. You'll work closely with data scientists, product, and platform engineering to take prototypes from a notebook to something a brand team relies on everyday.
You'll thrive here if you enjoy living at the seam between LLMs, traditional ML, and well-engineered backend systems - and if you care about making AI useful, reliable, and measurably better in a regulated, real-world domain.
- Design, build, and ship production-grade LLM and agentic AI systems powering anomaly detection, contextual synthesis, next-best-action recommendations, and one-click activation across the Daily Command product surface
- Build robust retrieval-augmented generation (RAG) pipelines over Doceree's clinical intent signals, campaign data, market data, and partner data - including chunking, embedding, indexing, hybrid retrieval, reranking, and grounding strategies
- Implement agent orchestration patterns (tool use, planning, multi-step reasoning, structured outputs, guardrails) using modern frameworks (e.g., LangGraph, LlamaIndex, custom orchestration) and own the latency, cost, and reliability of those agents end-to-end
- Build evaluation and observability for AI features - offline eval sets, golden datasets, LLM-as-judge harnesses, regression tests, online metrics, prompt/version tracking, and trace inspection
- Engineer prompts, system instructions, and tool interfaces as first-class artifacts - versioned, tested, and tied to measurable business outcomes (lift, NBRx correlation, decision time, action acceptance rate)
- Integrate and benchmark multiple model providers (OpenAI, Anthropic, Google, open-source via vLLM/TGI), and make pragmatic build-vs-buy and model-routing decisions based on quality, latency, and unit economics
- Partner with data scientists to productionize ML models (classification, ranking, recommendation, embedding, forecasting) - wrapping them in services, pipelines, and APIs that downstream agents and product surfaces can consume
- Own scalable, cloud-native deployment of AI services on AWS, with strong MLOps/LLMOps hygiene: CI/CD for models and prompts, feature/vector stores, monitoring, drift detection, cost guardrails, and safe rollout
- Implement safety, privacy, and compliance controls appropriate for healthcare/HCP data - PII/PHI handling, prompt injection defenses, output validation, auditability, and human-in-the-loop where it matters
- Collaborate with product and design to translate fuzzy user problems into well-scoped AI subsystems, then prototype quickly, evaluate honestly, and harden the winner
- Stay current with advances in LLMs, agents, retrieval, evaluation, and inference infrastructure, and bring back what's genuinely useful
- B.Tech / M.Tech / Ph.D. in Computer Science, Information Technology, Statistics, or related quantitative discipline from a Tier 1/2 institution
- 2-8 years of experience building and shipping AI/ML systems in production, with at least 2+ years focused on LLM-based or agentic AI applications (RAG, tool-using agents, copilots, structured generation, evaluation)
- Strong programming fundamentals in Python, with solid software engineering practices (typing, testing, code review, modular design, performance awareness). Comfortable in SQL and working with cloud-native data systems
- Hands-on experience with one or more LLM/agent frameworks (LangChain/LangGraph, LlamaIndex, Haystack, Semantic Kernel, or well-reasoned custom stacks) and vector databases (pgvector, Pinecone, Weaviate, OpenSearch, FAISS, etc.)
- Practical experience with prompt engineering, structured outputs (JSON schemas / function calling), tool use, and evaluation - including building your own eval harnesses rather than relying solely on vendor dashboards
- Working knowledge of classical ML/DL libraries (scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, SpaCy, NumPy, Pandas) and when not to reach for an LLM
- Experience deploying services on AWS (Lambda, ECS/EKS, SageMaker, Bedrock) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, LangSmith, Langfuse, Arize, or equivalents)
- Demonstrated ability to operate on noisy, incomplete, and sensitive real-world data, and to design systems that fail safely, observably, and recoverably
- Strong product instincts - you can push back on a half-formed requirement, scope an MVP, and know when "good enough to ship and learn" beats "perfect on a benchmark"
- Excellent written and verbal communication; able to explain agent behavior, eval results, and trade-offs to non-technical stakeholders, and to write design docs the rest of the team will actually read
- Experience building multi-agent or workflow-based AI systems (planner/executor patterns, critic/verifier loops, long-running agents with state)
- Familiarity with inference optimization - quantization, batching, KV cache reuse, speculative decoding, serving with vLLM/TGI/Triton, or building latency/cost-aware model routers
- Solid grounding in the math behind ML and LLMs - linear algebra, probability, statistics, and a working understanding of how transformers and embeddings actually behave
- Experience with healthcare, life sciences, or programmatic advertising data - claims, Rx, EHR-derived signals, HCP identity, campaign telemetry, attribution. Awareness of HIPAA / GDPR / DPDP considerations is a plus
- Familiarity with containerization and orchestration (Docker, Kubernetes), event/streaming systems (Kafka, Kinesis), and big-data tooling (Spark, Aerospike, DuckDB)
- Open-source contributions, public technical writing, or a portfolio of shipped AI features you can walk us through end-to-end
Doceree has been recognized and certified Best places to work NJ 2023 and Great Place to work 2026. If you are passionate about health technology and have a knack for turning complex concepts into compelling narratives, we invite you to apply for this exciting opportunity to contribute to the success of our innovative health tech company.
Below are the competitive benefits that will be provided to the selected candidates basis their location.
- Competitive Salary Package
- Generous Leave Policy
- Flexible Working Hours
- Performance-Based Bonuses
- Health Care Benefits
Doceree DEI (Diversity, Equity & Inclusion) is to create and sustain a one team culture globally with a mission to provide equal opportunity to people from diverse social, cultural, physical and psychological backgrounds and do not discriminate on the basis of race, colour, religion, national origin, sex, age, citizenship status, disability status, genetic information, sexual orientation, or gender identity or expression of an otherwise qualified individual, or membership in any other class protected by applicable law. Doceree fosters an inclusive culture by inculcating a sense of belonging within our ONE team through appropriate behaviours, policies and systems, while also being fair to one and all. To support this Doceree has taken formal strides with a 5C model and 4E's pledge, to ensure, encourage and exhibit our commitment to DEI, and move towards our DEI vision.
